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Why Orgs Should Consider Healthcare Storage Tools for AI, Analytics

December 07, 2017 - Artificial intelligence (AI) for data analytics demands IT infrastructure tools that will support the computing power those tools require. AI also involves large amounts of data being collected, which is why organizations must consider necessary healthcare storage tools. Available supercomputers can also help support AI and big data analytics.

Healthcare organizations collecting patient data using Internet of Things (IoT) and other patient monitoring tools need servers with the computing power to handle that much data. Continuously adding on traditional servers will be costly and those servers do not have the computing power necessary to support AI tools and make patient data actionable.

Many vendors are releasing computing tools that will help healthcare organizations store and process data and support the AI tools needed for big data analytics.

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IBM recently unveiled its Power Systems Servers for compute-intensive AI workloads. The servers compute power allows servers to improve the training times of deep learning frameworks so organizations can build AI applications faster.

“Deep learning is a fast growing machine learning method that extracts information by crunching through millions of processes and data to detect and rank the most important aspects of the data,” explained IBM in its official product release. “To meet these growing industry demands, four years ago IBM set out to design the POWER9 chip on a blank sheet to build a new architecture to manage free-flowing data, streaming sensors and algorithms for data-intensive AI and deep learning workloads on Linux.”

Deploying on-premises servers of this capacity may be unrealistic for many organizations, but this does not mean that super computing capabilities for AI and analytics are out of reach.

Back in October, Microsoft and Cray announced that Cray supercomputing systems will be supported in Microsoft Azure datacenters. Users can run heavy workloads such as advanced healthcare analytics and AI in their Microsoft Azure environment.

The availability of Cray supercomputers in Azure gives entities the ability to train AI deep learning models, which can be especially useful for medical imaging and research.

“Pharmaceutical and biotech scientists driving precision medicine discovery can now perform whole genome sequencing, shortening the time from computation to cure,” said Cray in its official release.

“Dedicated Cray supercomputers in Azure not only give customers the breadth of features and services in enterprise cloud, but also the advantages of running a wide array of workloads on a supercomputer, the ability to scale applications to unprecedented levels, and the performance and capabilities previously only found in the largest on-premise supercomputing centers,” Cray President and CEO Peter Ungaro said in a statement.

IT infrastructure tools that support healthcare AI and analytics are emerging, but many organizations are still just considering the possibility of AI as part of their infrastructure. Implementing these tools is a large and unfamiliar undertaking for IT departments and wrong implementation can present inaccurate analytics data and be a very costly mistake.

Dell EMC recently released its machine learning and deep learning solutions that let organizations use high performance computing (HPC) to process images and support personalized medicine.

The Texas Advanced Computing Center (TACC) at the University of Texas at Austin has already used Dell EMC’s technology to conduct research identifying brain tumors. The Center is using machine learning as one of the first applications of its new “Stampede2” supercomputer with Intel Xeon Phi 7250 processors across 4,200 nodes connected with Intel Omni-Path Fabric.

AI tools are complex and intimidating to introduce into a health IT infrastructure, but vendors are recognizing the challenges and releasing tools to make AI tools more accessible for the healthcare space.